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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #409593

Research Project: Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: Dry matter intake in US Holstein cows: exploring the genomic and phenotypic impact of milk components and body weight composite

item Toghiani, Sajjad
item Vanraden, Paul
item Baldwin, Ransom - Randy
item WEIGEL, KENT - University Of Wisconsin
item WHITE, HEATHER - University Of Wisconsin
item PENAGARICANO, FRANCISCO - University Of Wisconsin
item KOLTES, JAMES - Iowa State University
item SANTOS, JOSE EDUARDO - University Of Florida
item PARKER GADDIS, KRISTEN - Council On Dairy Cattle Breeding
item VANDEHAAR, MICHAEL - Michigan State University
item TEMPELMAN, ROBERT - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/26/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Feed efficiency in the dairy cattle industry represents a significant opportunity to enhance sustainability and herd profitability through genetic selection. An investigation into feed intake prediction using genomic regressions on milk components and body weight composites from the national U.S. genomic evaluation revealed more accurate and precise predictions regarding feed intake and maintenance costs compared to estimates derived from phenotypic regressions. The 2021 net merit index was subsequently revised to better account for these estimated regressions. Consequently, the use of this index should be more profitable when selecting dairy cows for smaller size, improved feed efficiency, and higher milk production.

Technical Abstract: Large datasets allow estimating feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake (DMI) records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk, age-parity subclass, trial date, management group, and body weight change during 28- and 42-day feeding trials in mid-lactation. Phenotypic regressions of DMI on milk (0.014±0.006), fat (3.06±0.01), and protein (4.79±0.25) were much less than corresponding genomic regressions (0.08±0.03, 11.30±0.47, and 9.35±0.87) or sire genomic regressions multiplied by 2 (0.048±0.04, 6.73±0.94, and 4.98±1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. The energy-corrected milk formula assumes that 69% more DMI is required for fat than protein production, but the genomic regression was only 21% more, the sire genomic regressions were about 35% more DMI for fat than protein, and the phenotypic regression estimated 56% more DMI required for protein than fat. Estimates of annual maintenance in kg DMI / kg body weight/lactation were similar from phenotypic regression (5.9±0.14), genomic regression (5.8±0.31), and sire genomic regression multiplied by 2 (5.3±0.55) and are larger than those estimated by NASEM (2021) based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with body weight and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The net merit formula was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.